{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score, roc_curve\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load features and labels\n",
    "df_feat = pd.read_csv('feature.csv', index_col=0)\n",
    "df_label = pd.read_csv('label.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# split training/test name\n",
    "unique_names = list(set([path.split('/')[0] for path in df_feat.index]))\n",
    "name_train, name_test = train_test_split(unique_names, test_size = 0.1, random_state = 0)\n",
    "name_train, name_test = set(name_train), set(name_test)\n",
    "# split training/test images\n",
    "idx_train = [path.split('/')[0] in name_train for path in df_feat.index]\n",
    "idx_test = [path.split('/')[0] in name_test for path in df_feat.index]\n",
    "X_train, Y_train = df_feat[idx_train], df_label[idx_train]\n",
    "X_test, Y_test = df_feat[idx_test], df_label[idx_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 40.40871057\n",
      "Iteration 2, loss = 35.03724240\n",
      "Iteration 3, loss = 32.83515467\n",
      "Iteration 4, loss = 31.91957083\n",
      "Iteration 5, loss = 31.30807016\n",
      "Iteration 6, loss = 30.93741692\n",
      "Iteration 7, loss = 30.71253075\n",
      "Iteration 8, loss = 30.52499209\n",
      "Iteration 9, loss = 30.34395784\n",
      "Iteration 10, loss = 30.18292498\n",
      "Iteration 11, loss = 30.05022947\n",
      "Iteration 12, loss = 29.90303121\n",
      "Iteration 13, loss = 29.77655242\n",
      "Iteration 14, loss = 29.63267736\n",
      "Iteration 15, loss = 29.52772159\n",
      "Iteration 16, loss = 29.38355494\n",
      "Iteration 17, loss = 29.28450222\n",
      "Iteration 18, loss = 29.19643486\n",
      "Iteration 19, loss = 29.09821095\n",
      "Iteration 20, loss = 29.03292399\n",
      "Iteration 21, loss = 28.95726096\n",
      "Iteration 22, loss = 28.92244591\n",
      "Iteration 23, loss = 28.85931823\n",
      "Iteration 24, loss = 28.82356971\n",
      "Iteration 25, loss = 28.76245395\n",
      "Iteration 26, loss = 28.72557219\n",
      "Iteration 27, loss = 28.66193364\n",
      "Iteration 28, loss = 28.62997854\n",
      "Iteration 29, loss = 28.59817352\n",
      "Iteration 30, loss = 28.58167145\n",
      "Iteration 31, loss = 28.52321373\n",
      "Iteration 32, loss = 28.52000127\n",
      "Iteration 33, loss = 28.46644273\n",
      "Iteration 34, loss = 28.44286234\n",
      "Iteration 35, loss = 28.41560849\n",
      "Iteration 36, loss = 28.38558485\n",
      "Iteration 37, loss = 28.36344334\n",
      "Iteration 38, loss = 28.32292745\n",
      "Iteration 39, loss = 28.30321816\n",
      "Iteration 40, loss = 28.27120268\n",
      "Iteration 41, loss = 28.23098477\n",
      "Iteration 42, loss = 28.20591958\n",
      "Iteration 43, loss = 28.19466065\n",
      "Iteration 44, loss = 28.17184207\n",
      "Iteration 45, loss = 28.14625867\n",
      "Iteration 46, loss = 28.10376324\n",
      "Iteration 47, loss = 28.10401394\n",
      "Iteration 48, loss = 28.05389262\n",
      "Iteration 49, loss = 28.03211734\n",
      "Iteration 50, loss = 28.01594439\n",
      "Iteration 51, loss = 27.99595619\n",
      "Iteration 52, loss = 27.96683778\n",
      "Iteration 53, loss = 27.96109584\n",
      "Iteration 54, loss = 27.94542292\n",
      "Iteration 55, loss = 27.92013270\n",
      "Iteration 56, loss = 27.89969468\n",
      "Iteration 57, loss = 27.88533823\n",
      "Iteration 58, loss = 27.84118057\n",
      "Iteration 59, loss = 27.82694994\n",
      "Iteration 60, loss = 27.80498732\n",
      "Iteration 61, loss = 27.78975380\n",
      "Iteration 62, loss = 27.75675585\n",
      "Iteration 63, loss = 27.74691094\n",
      "Iteration 64, loss = 27.72320487\n",
      "Iteration 65, loss = 27.69860021\n",
      "Iteration 66, loss = 27.68090493\n",
      "Iteration 67, loss = 27.67006514\n",
      "Iteration 68, loss = 27.64242759\n",
      "Iteration 69, loss = 27.61902046\n",
      "Iteration 70, loss = 27.61721442\n",
      "Iteration 71, loss = 27.58954431\n",
      "Iteration 72, loss = 27.57713167\n",
      "Iteration 73, loss = 27.54461949\n",
      "Iteration 74, loss = 27.52764680\n",
      "Iteration 75, loss = 27.50247056\n",
      "Iteration 76, loss = 27.48147980\n",
      "Iteration 77, loss = 27.47290238\n",
      "Iteration 78, loss = 27.46752387\n",
      "Iteration 79, loss = 27.47289023\n",
      "Iteration 80, loss = 27.42240431\n",
      "Iteration 81, loss = 27.39054289\n",
      "Iteration 82, loss = 27.38663008\n",
      "Iteration 83, loss = 27.35312067\n",
      "Iteration 84, loss = 27.35532919\n",
      "Iteration 85, loss = 27.32905178\n",
      "Iteration 86, loss = 27.28977771\n",
      "Iteration 87, loss = 27.28112048\n",
      "Iteration 88, loss = 27.28084286\n",
      "Iteration 89, loss = 27.25708770\n",
      "Iteration 90, loss = 27.22976751\n",
      "Iteration 91, loss = 27.20988234\n",
      "Iteration 92, loss = 27.21141766\n",
      "Iteration 93, loss = 27.17797058\n",
      "Iteration 94, loss = 27.16820768\n",
      "Iteration 95, loss = 27.14932839\n",
      "Iteration 96, loss = 27.12470330\n",
      "Iteration 97, loss = 27.12566521\n",
      "Iteration 98, loss = 27.11361609\n",
      "Iteration 99, loss = 27.09488508\n",
      "Iteration 100, loss = 27.07948456\n",
      "Iteration 101, loss = 27.04942764\n",
      "Iteration 102, loss = 27.04143474\n",
      "Iteration 103, loss = 27.04399627\n",
      "Iteration 104, loss = 27.00791531\n",
      "Iteration 105, loss = 27.00163900\n",
      "Iteration 106, loss = 26.97148961\n",
      "Iteration 107, loss = 26.96270890\n",
      "Iteration 108, loss = 26.95755612\n",
      "Iteration 109, loss = 26.92726214\n",
      "Iteration 110, loss = 26.91845495\n",
      "Iteration 111, loss = 26.90216764\n",
      "Iteration 112, loss = 26.89679669\n",
      "Iteration 113, loss = 26.90125384\n",
      "Iteration 114, loss = 26.86992425\n",
      "Iteration 115, loss = 26.85920256\n",
      "Iteration 116, loss = 26.84339385\n",
      "Iteration 117, loss = 26.81971566\n",
      "Iteration 118, loss = 26.79479841\n",
      "Iteration 119, loss = 26.79576737\n",
      "Iteration 120, loss = 26.81587257\n",
      "Iteration 121, loss = 26.75841567\n",
      "Iteration 122, loss = 26.77474780\n",
      "Iteration 123, loss = 26.73617765\n",
      "Iteration 124, loss = 26.73245863\n",
      "Iteration 125, loss = 26.71139246\n",
      "Iteration 126, loss = 26.71240407\n",
      "Iteration 127, loss = 26.71216712\n",
      "Iteration 128, loss = 26.66797618\n",
      "Iteration 129, loss = 26.66321779\n",
      "Iteration 130, loss = 26.64953511\n",
      "Iteration 131, loss = 26.63141024\n",
      "Iteration 132, loss = 26.67541904\n",
      "Iteration 133, loss = 26.61798609\n",
      "Iteration 134, loss = 26.62021558\n",
      "Iteration 135, loss = 26.60342879\n",
      "Iteration 136, loss = 26.59116301\n",
      "Iteration 137, loss = 26.59473083\n",
      "Iteration 138, loss = 26.59917299\n",
      "Iteration 139, loss = 26.57084991\n",
      "Iteration 140, loss = 26.52163527\n",
      "Iteration 141, loss = 26.53363158\n",
      "Iteration 142, loss = 26.51521617\n",
      "Iteration 143, loss = 26.50329333\n",
      "Iteration 144, loss = 26.50290619\n",
      "Iteration 145, loss = 26.50136534\n",
      "Iteration 146, loss = 26.47245966\n",
      "Iteration 147, loss = 26.47143477\n",
      "Iteration 148, loss = 26.44282071\n",
      "Iteration 149, loss = 26.43532230\n",
      "Iteration 150, loss = 26.41876706\n",
      "Iteration 151, loss = 26.42741677\n",
      "Iteration 152, loss = 26.40492247\n",
      "Iteration 153, loss = 26.39590725\n",
      "Iteration 154, loss = 26.39852510\n",
      "Iteration 155, loss = 26.38331057\n",
      "Iteration 156, loss = 26.38094615\n",
      "Iteration 157, loss = 26.39598398\n",
      "Iteration 158, loss = 26.37489629\n",
      "Iteration 159, loss = 26.32936084\n",
      "Iteration 160, loss = 26.35121908\n",
      "Iteration 161, loss = 26.29980141\n",
      "Iteration 162, loss = 26.32465961\n",
      "Iteration 163, loss = 26.28497291\n",
      "Iteration 164, loss = 26.31980047\n",
      "Iteration 165, loss = 26.29488513\n",
      "Iteration 166, loss = 26.26571148\n",
      "Iteration 167, loss = 26.23435835\n",
      "Iteration 168, loss = 26.23776221\n",
      "Iteration 169, loss = 26.21801322\n",
      "Iteration 170, loss = 26.21435610\n",
      "Iteration 171, loss = 26.21043853\n",
      "Iteration 172, loss = 26.21053820\n",
      "Iteration 173, loss = 26.21175730\n",
      "Iteration 174, loss = 26.18062537\n",
      "Iteration 175, loss = 26.16617010\n",
      "Iteration 176, loss = 26.16640111\n",
      "Iteration 177, loss = 26.17102895\n",
      "Iteration 178, loss = 26.15460464\n",
      "Iteration 179, loss = 26.12441810\n",
      "Iteration 180, loss = 26.11401805\n",
      "Iteration 181, loss = 26.11415764\n",
      "Iteration 182, loss = 26.12858125\n",
      "Iteration 183, loss = 26.09217862\n",
      "Iteration 184, loss = 26.11883554\n",
      "Iteration 185, loss = 26.09885372\n",
      "Iteration 186, loss = 26.08414448\n",
      "Iteration 187, loss = 26.04094102\n",
      "Iteration 188, loss = 26.07182495\n",
      "Iteration 189, loss = 26.07689494\n",
      "Iteration 190, loss = 26.05195578\n",
      "Training loss did not improve more than tol=0.000100 for two consecutive epochs. Stopping.\n"
     ]
    }
   ],
   "source": [
    "clf = MLPClassifier(solver='adam', hidden_layer_sizes=(128, 128),max_iter = 5000, verbose=True, tol=1e-4, activation='relu')\n",
    "clf.fit(X_train, Y_train)\n",
    "pred = clf.predict(X_test)\n",
    "score = clf.predict_proba(X_test)\n",
    "\n",
    "df_pred = pd.DataFrame(pred, columns=df_label.columns, index=Y_test.index)\n",
    "df_score = pd.DataFrame(score, columns=df_label.columns, index=Y_test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# save model\n",
    "import pickle\n",
    "save_path = 'face_model.pkl'\n",
    "with open(save_path, 'w') as f:\n",
    "    pickle.dump([clf, df_label.columns.tolist()], f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_roc(attr, target, score):\n",
    "    \"\"\"Plot a ROC curve and show the accuracy score and the AUC\"\"\"\n",
    "    fig, ax = plt.subplots()\n",
    "    auc = roc_auc_score(target, score)\n",
    "    acc = accuracy_score(target, (score >= 0.5).astype(int))\n",
    "    fpr, tpr, _ = roc_curve(target, score)\n",
    "    plt.plot(fpr, tpr, lw = 2, label = attr.title())\n",
    "    plt.legend(loc = 4, fontsize = 15)\n",
    "    plt.title(('ROC Curve for {attr} (Accuracy = {acc:.3f}, AUC = {auc:.3f})'\n",
    "               .format(attr = attr.title(), acc= acc, auc = auc)),\n",
    "              fontsize = 15)\n",
    "    plt.xlabel('False Positive Rate', fontsize = 15)\n",
    "    plt.ylabel('True Positive Rate', fontsize = 15)\n",
    "    plt.show()\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAFtCAYAAAATY4N4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nc78ADolDMcyyGa8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12877bd390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAFtCAYAAAATY4N4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlYVNX/B/D3sFkJKZiogSRiYIFsokiiyOKGC+7gV9Rc\ns9RUNEVMwV3LLbcsLVcQTUlzRTItUwjMwkTNRCUgVBAhkZ05vz98mF8jwzgoM8Po+/U8PA9zz517\nP3PFec855947EiGEABEREeksPW0XQERERM+GYU5ERKTjGOZEREQ6jmFORESk4xjmREREOo5hTkRE\npOMY5qQTdu3ahQ4dOqBXr17aLkVOZmYm2rRpg4SEBLXup6KiAiNGjMCKFSvUuh+iF0VBQQG6deuG\nb775Rtul1AqGuZoNHz4cb731Ftq0aSP7adu2LQYOHIhDhw5VWT8jIwMRERHw9fWFk5MT3NzcEBwc\njIMHDyrcfnx8PCZMmAB3d3c4OjqiS5cuCA0NxY0bN55YW2xsLEaOHIl27drB2dkZvr6+WLBgAe7e\nvfvMr7u2rV27Fv3798fhw4fVsv25c+fCzs4OUVFRNXqehYUF/vjjD3To0EEtdVVas2YNCgsLMXXq\nVLnlRUVFaNeuHZycnJCXl6fWGp4Xly5dwrvvvgt3d3d4enoiJCQEubm51a6fl5eHiIgIeHt7w8nJ\nCUOGDEFycrLcOnfu3MHUqVPRtm1buLq6YuzYsUhPT5dbZ/PmzfDx8YGjoyP8/f3x3XffPVX9mzZt\ngp2dHT799NMqbTExMbCzs0N5eXmVtl9++QV2dnZIS0uTLSstLcWWLVvQv39/uLi4wNXVFX379sWm\nTZtQWlr6VPVVp6ioCBEREfDx8UHbtm0RGBiIs2fPKn1OTEwMAgIC4OLigi5duuDLL7+sdt3Dhw/D\nzs4OMTExCtvz8vLg6emJ4cOHAwCMjY2xevVqLFmyBNeuXXv6F1ZXCFKr4OBgMX36dLllRUVFIiYm\nRrz99tvi+PHjsuXJycmibdu2YtasWeLGjRtCKpWKhw8fiiNHjoiOHTuKWbNmyW1nx44dok2bNmLb\ntm0iNzdXVFRUiOvXr4uQkBDh7Ows/vjjj2rrWrZsmXBzcxPffvutePDggSgrKxOXLl0So0aNEh4e\nHuKff/6p3QPxjGxtbcXevXvVsu38/Hzh5OQkPvroI9G7d2+17ONZZGRkCHt7e3H27NkqbdHR0cLf\n31/0799ffPXVV1qoTrfcv39fuLu7i08//VT8+++/Ijs7W4wZM0YEBwdX+5yxY8eKvn37ips3b4ri\n4mIRGRkp2rZtK7Kzs4UQQpSWlorevXuLmTNninv37ol79+6JOXPmiNDQUNk2vvjiC+Ht7S2Sk5NF\ncXGxOHbsmOjRo4fIysqqUf3l5eXCy8tLfPTRR8Ld3V2UlJTIte/fv1/Y2tqKsrKyKs9NSEgQtra2\n4tatW0IIIUpKSkRQUJDo1auXOHfunCgtLRWFhYXi9OnTwtfXV/zvf/8T5eXlNapPmdDQUNG3b19x\n48YNUVxcLHbv3i0cHBxEamqqwvWPHj0q3nrrLXHs2DFRWloqUlJShI+Pj4iKiqqybnZ2tvDw8BDO\nzs5i//79Crc3ffp00bZt2yr/1iEhIWLs2LHP/gK1jGGuZorCvNLo0aPFe++9J4QQoqKiQvTs2VOM\nGzdO4bpXrlwRb731liz8MzMzhb29fbVv4IsWLRLff/+9wrYLFy4IW1tbuQ8SlUpKSsTs2bNFUlJS\ntfUHBQXJPljs379ftG/fXuzatUu0bdtWrFu3Ttja2opffvlF7jlr164VXl5eoqKiQhQVFYmFCxcK\nb29v0aZNG9GjRw/x7bffKqz19u3bwsHBQdja2oq3335bdOvWTQjxKOAmTpwoOnbsKBwdHcWQIUNE\nQkKC7HnBwcFi/vz5YvTo0cLJyUnpm9LWrVtFt27dRE5Ojnj77bdlr73S33//Ld577z3Rvn174ezs\nLAICAkRcXJwQQoj09HRha2srC9qCggIxd+5c4enpKZydnYW/v784fPiw3HEYNGiQOHLkiOjWrZtw\ncnISQUFB4ubNm9XWt2zZMuHv76+wrU+fPuLzzz8XW7duFV27dhVSqVSuPTs7W4SEhAg3Nzfh7u4u\nQkJCxL1792TtBw4cEL169RJOTk6iV69ecrU+/gGqrKxM2Nrayt4sZ82aJSZNmiRmzJghnJ2dxd9/\n/y3KysrEp59+Kry9vYWzs7Pw9fUV27Ztk6vpp59+Ev379xdOTk7Cz89PbN++XQghxMyZM0VgYKDc\nuiUlJaJdu3Zi165dVV77hg0bhIODg8KfUaNGKTxeO3fuFO3bt5cLuytXrghbW1tx5cqVKus/fPhQ\n2NnZiUOHDsktDwwMFF9++aUQQogjR46I9u3bi6KiIoX7LCkpEW5ubuLIkSMK22vixIkTwtXVVfz7\n77/C3d29yv+bmoT5pk2bhKOjo8IPFDdv3hShoaEK2zIyMqo97g4ODiIjI6PKc/Ly8oS9vb3s/02l\ngIAAsXjxYoWv9cMPPxSjR4+WWxYdHS169uxZZd0PPvhA9p6iKMzj4uJEp06dxNKlS6uEeXJysrC1\ntRXXrl1TWIeu4DC7FpWWluKll14CAFy5cgWpqakYN26cwnVbt26N9u3by4bmYmNjoaenh+DgYIXr\nz5kzB76+vgrbjhw5AktLS3Tv3r1Km5GREZYsWQI3NzeVX0dJSQlSUlLw448/YuLEiXB0dMSxY8eq\n7DMgIAB6enqYN28ekpOTsX37dly4cAEhISGYM2cOkpKSqmy7SZMm+OOPPwAAERERiI2NRXl5OUaP\nHg1DQ0McOnQIv/zyC9zd3TF+/HhkZmbKnnvs2DEMGTIEFy5cgL6+vsLahRCIiorCwIED0ahRI3h7\neyMyMlJunYiICDRs2BCnT59GUlISRo0ahY8++kjhsPaqVavw66+/4ttvv8X58+cxfPhwzJw5E7du\n3ZKtc+vWLcTHx2Pfvn04deoUCgoKsGrVqmqP788//4x33nmnyvLz58/j+vXr6N+/P/r27Yt//vkH\nZ86ckVtn0qRJKCkpQVxcHGJjY5Gbm4uQkBDZdufNm4fQ0FCcP38eISEhmDlzJs6fP19tLY9LSkqC\nvb09kpKSYGlpiR07diAmJgbbtm3DhQsXMHfuXCxZskR2TsG1a9fwwQcf4N1330ViYiI+/fRTrF69\nGgcPHsTgwYPx22+/yU0RnTlzBsXFxejTp0+VfX/wwQf4448/FP58/fXXCuv9/fffYW9vDwMDA9ky\nOzs71KtXD7///rvC50gkEkilUrllDRs2lP1dJiQk4K233sKmTZvQqVMneHh4YPr06bh37x4AICUl\nBf/++y/KysrQv39/uLq6YuDAgU8cYlZk165d6NWrF0xMTBAQEFDjaaH/Onz4MPz9/dG0adMqbS1a\ntMDSpUsVtlVOLVX3Y2FhUeU5KSkpKCsrQ5s2beSWOzo6VpmyqKTouJuamiI1NRUPHz6ULTt06BCu\nXr0q+7t+XOU0ycKFC1G/fv0q7Q4ODmjYsCF++uknhc/XFQxzLSgoKEBUVBTOnz+PAQMGAIBsHsvG\nxqba57Vs2RI3b94E8CgQrKysYGRkVOP937p1C61atXqKyhUrKirCyJEjUb9+fUgkEvTt2xcnTpyQ\n/Ue8fPkybt68iX79+iEvLw+HDh3ClClT0Lx5cxgYGKBr167w8fHB3r17VdrfmTNnkJaWho8//him\npqZ46aWXMHnyZLz00ks4evSobL1mzZqhe/fu0NOr/s/8zJkzyMzMRP/+/QEAgwcPRlxcHLKzs2Xr\nPHjwAPr6+jAyMoKBgQECAgJw4cIFNGzYsMr2Zs2ahejoaLz22mvQ19dHQEAAysvLkZKSIlunoKAA\ns2bNgomJCUxNTdGpUyf8+eefCuurqKjAX3/9BTs7uyptkZGR6NSpE5o0aQIzMzP4+vrKvblfvXoV\nv/32GyZPnoyGDRuiQYMGmD9/PoYOHQohBHbv3o3OnTvD09MTBgYG8PHxwWeffQZTU1MlR1+eRCLB\niBEjYGBgAIlEguHDh+PYsWOwsrKCRCKBl5cXzMzMcPHiRQDAvn378Oabb6Jv374wMjKCs7Mz1q9f\nj1atWsHNzQ2tWrXCvn37ZNs/cuQIunbtildffVXlmpS5f/8+GjRoUOU1NGjQQBa+//XKK6/A09MT\nmzdvRmpqKkpLS3Hs2DH8+uuvuH//PgAgKysLv/32GwwMDHDixAlERkbi+vXrsnDJysoCAOzfvx9r\n167FTz/9hA4dOuC9996Tm79+ktTUVCQkJGDw4MEAHv2tJicny/1t1URaWlqtvg8oU3lOwuP/Z0xN\nTRUedwDo1q0bEhIScOTIEZSWluLGjRvYvn07AMg+SGdnZ2Px4sVYvHgxXnnlFYXbWbhwITw9PeHl\n5aWwXU9PD7a2trhy5cpTvba6gmGuAUeOHKlyAtz+/fuxbt06dO7cWW7dxz+J/ldFRQUkEgmAR29A\nhoaGT1XPszy3OlZWVrLfe/Xqhby8PCQmJgJ41ANwdnaGtbU10tLSIJVKMWHCBLljcurUKfzzzz8q\n7SstLQ1mZmZo1KiRbJmhoSGsrKzkTjpq3rz5E7cVGRkJLy8vNG7cGADQqVMnvPbaa3IfLKZOnYpT\np06hU6dOmD59Og4ePIiysjKF28vKykJoaCg6dOgABwcHtG/fHsCj0YtKjRo1grGxsezxyy+/jKKi\nIoXby8vLgxCiSsDevXsXcXFxsjd2ABgyZAh+/PFH2ehE5WiApaWlbB0rKyt0794dEokEaWlpcm0A\n4Ofnp/QD5eMsLCzkPiw9ePAAixYtgqenp+zfNjc3V/b6Fe2zY8eOsLe3B/AooA4ePIjy8nIUFhbi\nhx9+wKBBg1Su51lU/t963PLly9G6dWsMHz4c3t7eSExMRJ8+fWT/hyr/fSZNmoSXX34ZLVu2xLRp\n05CQkCALcgB4//330bx5cxgbGyMkJAQNGjSo0QmdkZGRaN26tax326pVK7i4uFQZSarJ663t94Gn\nrUMRf39/zJkzB+vWrUOHDh0QHh6OIUOGAIBsZCU8PBw9evSo9gTU77//HomJiQgLC1Nag5mZmezD\nma4yePIq9Kx69eolu6RIKpXif//7Hxo2bAg/Pz/ZOi1btgQA/PXXX3jttdcUbic1NVX2RtuyZUt8\n++23ePjwocKhI2VatmyJuLg4SKVSpb3W6ij6wPHfNwUzMzN4enri2LFjcHd3x7FjxzB+/HgAQL16\n9QAAe/fuxdtvv13jfQOPpieEgi/7e7yuJ71Rpaen46effoKhoaHctEJxcTH27t2LCRMmQF9fH++8\n8w5Onz6NX375BefOncPKlSuxadOmKpe0SKVSjBkzBhYWFti3bx8sLCwUDi0+zTF/3J49e1BWVoZZ\ns2bJvRlKpVJER0dj+vTpsqkFRceqsg5lHx4f96R/dwCYMmUK8vLysH37dlhbW0NPTw+enp4q77Nf\nv35YuXIlfvzxRxQXF6NRo0bVvlFv3LgRn3/+ucK2du3aKRxqb9SokdyoC/Do+OTn58s+0D3OzMys\nypnjH374IV5//XUAgLm5eZVtVn6QvH37NszNzQHI90r19fVhYWGBO3fuKNzn4woKCnDgwAGUlpbK\n/a2WlJTg6tWrCA0Nxauvvir79ygqKoKJiYncNh48eAAAsqm9li1byqYKaiIzMxM9evSotv348eNV\nhtorP3jn5eWhSZMmsuX379+v9v0OAIKDg+WmEk+fPo169eqhUaNG+O6773D16lWFZ/VX7qtyeL22\nRnbqMvbMNUxPTw9Lly5FfHw8oqOjZctbt26N1q1bV/vmdPHiRSQlJaFfv34AgO7du0MqlWLz5s0K\n1582bRrWrVunsK13797IzMzE/v37q7SVl5dj+PDhsp5pvXr1UFxcLGuXSqXIyMh44usMCAjAyZMn\nceHCBeTk5MDf3x/Aozc5fX19XL58WW79f/75R+HlNIq0aNEC9+/fl7uErrS0FH///bfsQ5EqoqKi\n0LRpUxw9ehQHDhyQ/URHRyM7Oxs//PADgEdDhEZGRujUqRNmzZqFo0eP4vbt2zh37pzc9u7du4f0\n9HQMGzYMlpaWkEgk1c4Hqqphw4aQSCRyvYaysjLs2bMHo0aNwsGDB+VqnzBhAvbt24fS0lK0aNEC\nAOTmoP/++298/fXXKC8vR4sWLapcwnjgwAHZuQv16tWTGzFQZUj4999/x4ABA2BjYwM9PT1kZmbK\nBZ2ifZ48eRKnTp2Svd5u3brhyJEjOHToEAYMGFBtz+1p5sxdXFxw+fJluZGVP/74AyUlJXB1dVX4\nnJ9++kk2TQA8CtDK8zQAyC73qgxL4NFxBh6NitjY2MDAwEAuOCsqKpCZmVlllKI6Bw8ehBCiyr/3\n4cOHYWhoKLsc68033wTw6PK7xyUmJuK1116TfWjp06cPjh49qvAy1tu3b6N79+4Kh/CfZs7cwcEB\nRkZGVc5LuHDhQrXn56SlpVUZuTh9+jTc3NxgYGCAb775Bvfu3YOPjw/c3d3h7u6OrKwsLFy4EO+/\n/z5OnTqFnJwchIaGytq3bNmCCxcuyNatlJubq3DaTJcwzLXA2toa06ZNw/Lly+VOjFq+fDmuXr2K\n8ePHIzU1FUIIFBYW4tixY5g4cSKGDh0KHx8fAI9ODPv444/x5ZdfYvny5cjOzoZUKsWNGzcQEhKC\nxMRE9OzZU+H+27RpgwkTJmD+/Pn48ssvkZ+fj4qKCqSkpGDcuHG4e/cuvL29ATz69P7rr78iMzMT\nJSUlWLdunUqh6+Pjg6KiIqxduxY+Pj6yecr69etj0KBB2LBhAy5fvoyKigokJSWhf//+cvPdynh5\neaFZs2ZYtGgR/v33Xzx8+BArVqyAVCqVfWh4kuLiYsTExCAwMBCWlpZyPw4ODvDy8kJkZCQKCwvR\nrVs3fP311ygqKoJUKsXFixflwrKSqakpjI2N8dtvv6G8vBwXL17E1q1bUb9+fZWnEB6nr6+PN998\nU25OPS4uDrm5uRg5cmSV2keMGIEHDx7g2LFjePPNN9GuXTusXr0aOTk5ePDgAZYuXYoff/wRBgYG\nGDp0KOLj43HixAmUlZXh7NmzmDt3rmw/LVu2xMmTJ1FYWIjc3Fxs3LjxiaMdVlZWSE5ORmlpKVJT\nU7F48WJYWFjIXv+QIUOQlpaGyMhIlJaWIiUlBbNnz0Z+fr5sG4GBgfjhhx/w888/y84pqS29e/eG\noaEhVq1ahYKCAty+fRuffPIJunTpIhv1WrlyJaZPny57zg8//IBZs2bh9u3bKCwsREREBMzMzGQn\nkPbr1w+vvPIKIiIikJ+fj4yMDKxZswbdunVD48aNYWpqigEDBmD9+vVISUlBcXExPvvsMxQWFso+\nnMfFxaFHjx6oqKhQWHdkZCR69+4NGxsbuX/vN954AwEBAdi9ezeEEGjdujUGDBiA8PBwJCUloays\nDP/++y927NiByMhIzJgxQzYyNHz4cLi6umLEiBGIjY1FaWkpiouL8eOPPyI4OBg2NjZ46623auW4\nm5iYYODAgVi3bh1u3ryJoqIifPXVV8jMzERQUBCARx2WHj16yP5W8vLy8NFHHyE2NhZSqRTff/89\nYmJi8N577wEAPvvsM8TGxuLgwYOyH3Nzc0yZMgWLFy9Gjx49cPr0abn2oKAgODg4yNYFHnVQrl27\nVmuvVWu0dRr9i6K6S9MqKirE0KFDxaBBg+QuI8nIyBBz586VXbZVeV1kdZe1/PLLL7LLphwdHYWv\nr69YsGCBuH379hNrO3HihBg+fLho27atcHZ2Ft27dxerVq0S+fn5snVu374tRowYIZycnISnp6fY\ntm2bmDx5styladVdChMaGipsbW3FqVOn5JYXFhaK+fPnCw8PD+Ho6Ch69Oih8NKj/3r8MqnU1FQx\nbtw40aFDB9G+fXsxevRo8eeff8ralV0SKIQQ33zzjbC3txc5OTkK23/88UdhZ2cnUlNTRVJSkggM\nDBTOzs7CxcVFBAQEyC5VevzStNjYWOHt7S2cnJxEcHCwSEtLE8uWLRMODg5i06ZNYu3ataJTp05y\n+1K07L+WLVsmevXqJXv8v//9T7z//vvVrj916lTZJV73798XH374oXBxcRHt27cXU6dOlV0fLcSj\ny6r8/PxEmzZtRM+ePcXBgwdlbUlJScLf31+0adNG+Pv7i3Pnzgl3d3e5S9OCgoLk9p2UlCR69uwp\nHB0dxYABA8TFixfFtm3bhKOjo5g7d64QQoj4+HjRu3dv0aZNG+Hr6yu+/vrrKq+hW7duYsyYMdW+\nxmfx559/iuHDhwtHR0fRrl07ERoaKh48eCBrf/x1FRQUiOnTpws3Nzfh4uIiJkyYIDIzMxVu08nJ\nSbi5uYm5c+fKbbOkpEQsXLhQeHh4CAcHBzFkyBBx8eJFWXvl/6OKiooq9Z47d07Y2tqKy5cvK3w9\n169fF7a2tuLMmTOyfW3dulX07t1bODs7Cw8PD/Huu++Kc+fOVXluSUmJ+Oqrr0RAQICs9oEDB4qo\nqCiFtTyLymPQoUMH0aZNGzFkyBBx/vx5Wfvjl84JIcTevXuFj4+PaNOmjejVq5eIjY1Vuo/qLk2r\ntHbt2iqXpl28ePG5uDRNIkQ1E2pEVCdkZGSgR48e2Lx5Mzw8PLRdjtoVFRXBx8cHS5cuRZcuXbRd\njsYEBQXJTb2RZnz00Ue4f/8+tmzZou1SngmH2YnqOEtLS4wcORKrV6+udhj2eVFUVIT58+fjjTfe\nqPZSoufR1atX0axZM22X8cK5cuUKvv/+e8yYMUPbpTwz9syJdEDljXKcnJzk5nOfJ4cOHcKcOXPg\n5OSETz75hOFGalVQUICBAwdi7Nixcpd46iqGORERkY7jMDsREZGO08mbxhQXF+PSpUto3Lhxtffc\nJiIiep5UVFQgOzsbDg4Ospv/VNLJML906RKGDRum7TKIiIg0LjIyssrNdnQyzCvvYBQZGanwW32I\niIieN7dv38awYcMU3npYJ8O8cmi9adOmKt8OkYiI6HmgaHqZJ8ARERHpOIY5ERGRjmOYExER6TiG\nORERkY5jmBMREek4hjkREZGOY5gTERHpOI2G+bVr1+Dn54ddu3ZVaTt37hwGDRqEwMBAbNiwQZNl\nERER6TSNhXlhYSEWLlwIDw8Phe2LFi3CunXrsHv3bpw9exbXr1/XVGlEREQ6TWN3gDMyMsLmzZux\nefPmKm3p6elo0KCB7PuLvby8EB8fj1atWmmqPCJ6wczfkoDzV+5ouwx6jrm91QThYztoZF8a65kb\nGBhU+ZaXStnZ2TAzM5M9NjMzQ3Z2tqZKI6IXEIOcnic6eW92IqLacmhlgLZLIHpmdeJsdnNzc+Tk\n5Mge37lzB+bm5lqsiIiISHfUiZ65paUlCgoKkJGRgaZNm+LUqVNYsWKFtssiIi3gXDZRzWkszC9d\nuoTly5cjMzMTBgYGiI2NhY+PDywtLdG1a1dERERg+vTpAAB/f39YW1trqjQiqkM0GeRubzXR2L6I\n1EljYe7g4ICdO3dW296uXTvs2bNHU+UQUR3HuWwi1dWJYXYiqjs4zE2ke+rECXBEVHfUhSDn8DdR\nzbBnTkQKcZibSHcwzIleEBw+J3p+cZid6AVRkyDnMDeRbmHPnOgFw+FzoucPe+ZEREQ6jj1zIh3E\n+W8i+i/2zIl00NMGOefCiZ5P7JkT6TDOfxMRwDAnAsBhayLSbRxmJ0LduOtZTXHInIgqsWdO9B8c\ntiYiXcSeORERkY5jz5yeC5zzJqIXGXvm9FyojSDnHDQR6Sr2zOm5wjlvInoRMcxJIzgMTkSkPhxm\nJ43QRJBzmJyIXlTsmZNGcRiciKj2sWdOajd/S4K2SyAieq4xzEntKofYOQxORKQeDHPSmPCxHbRd\nAhHRc4lhTkREpON4Ahw9NV5uRkRUN7BnTk+tJkHO+XIiIvVhz5yeGS83IyLSLob5c4pD4ERELw4O\nsz+nNBXkHD4nItI+9syfcxwCJyJ6/jHMdRiH0omICOAwu057UpBzCJyI6MXAnvlzgEPpREQvNvbM\niYiIdBx75jqAc+NERKQMe+Y6QFmQc16ciIjYM9chnBsnIiJFGOZ1DIfUiYiopjjMXsdUF+QcTici\nouqwZ15HcUidiIhUxTDXIA6hExGROnCYXYNUDXIOqRMRUU2wZ64FHEInIqLaxJ45ERGRjmPPvJZx\nXpyIiDSNPfNaxm8yIyIiTdNoz3zJkiVITk6GRCJBWFgYHB0dZW2RkZH47rvvoKenBwcHB8yZM0eT\npdU6zosTEZGmaKxnnpiYiLS0NOzZsweLFy/G4sWLZW0FBQX46quvEBkZid27dyM1NRW///67pkqr\nNfO3JGi7BCIiegFpLMzj4+Ph5+cHALCxsUF+fj4KCgoAAIaGhjA0NERhYSHKy8tRVFSEBg0aaKq0\nWlM5xM6hdCIi0iSNhXlOTg5MTU1lj83MzJCdnQ0AqFevHiZOnAg/Pz94e3vDyckJ1tbWmiqt1oWP\n7aDtEoiI6AWitRPghBCy3wsKCvDFF1/g+PHjOHnyJJKTk3H16lVtlUZERKRTNBbm5ubmyMnJkT2+\ne/cuGjduDABITU1F8+bNYWZmBiMjI7i5ueHSpUuaKo2IiEinaSzMO3bsiNjYWABASkoKzM3NYWxs\nDACwsLBAamoqiouLAQCXLl1CixYtNFUaERGRTtPYpWmurq6wt7dHUFAQJBIJwsPDERMTAxMTE3Tt\n2hVjxozBiBEjoK+vDxcXF7i5uWmqNCIiIp2m0evMZ8yYIfe4devWst+DgoIQFBSkyXJqBe/4RkRE\n2sY7wD2jx4Ocl6UREZGm8d7stYR3fCMiIm1hmD8lDq8TEVFdwWH2p/TfIOfQOhERaRN75s+Iw+tE\nRKRt7JkTERHpOIb5U+C3oxERUV3CMH8K/HY0IiKqSxjmz4DfjkZERHUBw5yIiEjHMcyJiIh0HMOc\niIhIx/E68yfgnd6IiKiuY8/8CaoLcp7JTkREdQV75irind6IiKiuqnHPvLy8XB11EBER0VNSKcyl\nUik2bty7fylwAAAgAElEQVSILl26wNXVFQBQWFiIefPmobS0VK0FahPv9EZERLpApTBftWoV9u3b\nh7Fjx8qWFRcX4/Lly1ixYoXaitM23umNiIh0gUphfvjwYXz++ecIDg6GRCIBAJiZmWH16tWIi4tT\na4F1Ae/0RkREdZlKYZ6fnw87O7sqy5s1a4bc3NxaL4qIiIhUp1KYW1pa4rfffgMACCFky7///ns0\nbdpUPZURERGRSlS6NC0oKAjvv/8+AgMDIZVKsWPHDly+fBlHjx7FzJkz1V0jERERKaFSmA8bNgz1\n6tVDZGQk9PX1sWHDBrRo0QLLli2Dv7+/umskIiIiJVQK89zcXAwaNAiDBg2SW15aWorLly/j7bff\nVktxRERE9GQqzZl7e3srXF5aWooRI0bUakFERERUM0p75nFxcThx4gTKysoUzo1nZmZCX19fbcUR\nERHRkykN8xYtWqBRo0YQQiArK6tKu7GxMRYvXqy24rSJd38jIiJdoTTM33zzTYSGhiI7OxsrV65U\nuE56erpaCtM23v2NiIh0hUpz5tUFeVZWFvr371+rBdU1vPsbERHVdSqdzZ6RkYHZs2fj4sWLVb5Y\nxcbGRi2FERERkWpU6pkvWLAARkZGmDlzJvT19REREYF+/frByckJO3fuVHeNREREpIRKYZ6cnIzP\nPvsMw4YNg76+PgIDA7F06VL069cPX331lbprJCIiIiVUCnMAqF+/PgBAX18fRUVFAIC+ffvi22+/\nVU9lWjJ/SwL6TD+o7TKIiIhUplKY29raYs2aNSgrK0OLFi2wd+9eAMCtW7eqzKHrusqz2AGeyU5E\nRLpBpTCfMWMG9uzZg5KSErz77rtYtmwZ2rVrhyFDhqBHjx7qrlErDq0M4JnsRESkE1Q6m93JyQk/\n/fQTjIyM0LdvXzRp0gTJycmwsrJCt27d1F0jERERKaFSmAOAkZGR7Hd3d3e4u7sDAKRSae1XRURE\nRCp74jB7XFwcPvjgA4SEhODs2bNybX///TeGDh2qtuKIiIjoyZSG+eHDhzFt2jSUlpYiJycH48eP\nx6lTpwAAe/fuRUBAAAwNDTVSKBERESmmdJh9+/btWLRoEfr16wcAiIqKwsaNG7F3717Ex8dj6tSp\nGDlypEYKVbf5WxLkzmQnIiLSFUp75rdu3YK/v7/scd++ffHHH38gPz8fBw4cwLvvvguJRKL2IjWB\nl6QREZGuUtozLy0tlTvxzdjYGEZGRoiKilJ7YdpyaGWAtksgIiKqEZXvAFfpeemJExERPS9qHOZE\nRERUtygdZi8rK8PMmTOfuOyTTz6p/cqIiIhIJUrDvG3btsjKynriMiIiItIepWFe299VvmTJEiQn\nJ0MikSAsLAyOjo6ytqysLISEhKCsrAxvv/02FixYUKv7JiIiel5pbM48MTERaWlp2LNnDxYvXozF\nixfLtS9btgyjR4/Gvn37oK+vj3/++UdTpREREek0jYV5fHw8/Pz8AAA2NjbIz89HQUEBgEf3d//1\n11/h4+MDAAgPD8frr7+uqdKIiIh0msbCPCcnB6amprLHZmZmyM7OBgDk5uaifv36WLp0KYYOHYqV\nK1dqqiwiIiKdp7VL04QQcr/fuXMHI0aMwK5du3D58mWcPn1aW6URERHplBqFeWlpKdLT059qR+bm\n5sjJyZE9vnv3Lho3bgwAMDU1xeuvvw4rKyvo6+vDw8MDf/3111Pth4iI6EWjUpg/fPgQc+bMgaur\nK3r27AkAyM/Px5gxY5CXl6fSjjp27IjY2FgAQEpKCszNzWFsbAwAMDAwQPPmzXHr1i1Zu7W1dU1f\nCxER0QtJpTBfvnw5rly5grVr10JP79FT9PT0YGBggOXLl6u0I1dXV9jb2yMoKAiLFi1CeHg4YmJi\nEBcXBwAICwvD7NmzERQUBBMTE9nJcERERKSc0uvMK508eRLR0dFo3ry57N7sJiYmWLRoEfr376/y\nzmbMmCH3uHXr1rLf33jjDezevVvlbREREdEjKvXMi4qK0Lx58yrLX331VTx48KDWiyIiIiLVqRTm\n1tbW+OGHH6os379/P6ysrGq9KCIiIlKdSsPsY8eOxbRp09C1a1dUVFRgyZIluHLlCn799VesWLFC\n3TUSERGREiqFec+ePdGgQQNERUXBysoK58+fR4sWLRAVFQVnZ2d110hERERKqBTm8fHxeOedd/DO\nO++oux4iIiKqIZXmzEeNGgVvb2+sWbMGaWlp6q6JiIiIakClMP/+++8RFBSEU6dOoUePHggKCkJ0\ndDTPZCciIqoDVApzS0tLvPfeezh48CAOHz4MDw8PbNu2DZ6enpg2bZq6ayQiIiIlavxFKzY2Npg4\ncSLCwsLg4uKC48ePq6MuIiIiUpFKJ8ABQEVFBX7++WccP34cJ0+ehEQiQffu3TFp0iR11kdERERP\noFKYh4WF4eTJk3j48CG8vLywcOFCeHt7w8jISN31ERER0ROoFOY3btzA1KlT0bNnTzRs2FDdNRER\nEVENVBvmQgjZl6pERUXJlkul0irrVn6TGhEREWletWHu7OyM5ORkAMDbb78tC3ZFrly5UvuVERER\nkUqqDfMFCxbIfl+6dKlGiiEiIqKaqzbMAwICZL+/8sor6N69e5V1ioqKEBMTo57KiIiISCUqTXbP\nnDlT4fIHDx5g+fLltVoQERER1YzSs9m3bduGrVu3orS0FF26dKnSnp+fj2bNmqmrNiIiIlKB0jAf\nMmQIrKysMHnyZAwaNKhK+8svv4xu3bqprTgiIiJ6MqVh/sorr8DHxwdz585FUFCQpmoiIiKiGqg2\nzA8cOIB+/fo9WsnAAPv27at2I4p67URERKQZ1Yb5vHnzZGH+8ccfV7sBiUTCMCciItKiasP84sWL\nst+vXr2qkWKIiIio5lS+D2tqaqrs96ysLGzbtg1nzpxRS1FERESkOpXC/JtvvsHgwYMBAAUFBQgM\nDERkZCRmzJiByMhItRZIREREyqkU5lu3bsX69esBAEeOHMHLL7+Mo0eP4uuvv5b7EhYiIiLSPJXC\nPCsrC++88w4A4Oeff4a/vz8MDQ1hb2+PrKwstRZIREREyqkU5q+88goKCgpQWlqKxMREdOzYEcCj\nIXd9fX21FkhERETKKb1pTKUOHTpgypQp0NfXh4mJCdq2bYvy8nJs2LABbdq0UXeNREREpIRKPfO5\nc+fCwsICxsbG2LBhAyQSCYqKivDDDz9gzpw56q6RiIiIlFCpZ96wYUO57zcHABMTE8TGxqqlKCIi\nIlKdSmEOAMePH8e3336Lv//+GxKJBNbW1ggKCkKnTp3UWZ9GzN+SoO0SiIiInppKw+xRUVGYPn06\nJBIJfH190aVLF5SWlmLChAn44Ycf1F2j2p2/cgcA4PZWEy1XQkREVHMq9cx37NiBtWvXwtfXV275\n0aNHsXHjRvj4+KilOE0LH9tB2yUQERHVmEo989u3b8Pb27vK8m7duuHWrVu1XRMRERHVgEph3rhx\nY/z9999VlmdmZuLVV1+t9aKIiIhIdSoNs3t7e+PDDz/E5MmTYWNjAyEE/vzzT2zYsAGenp7qrpGI\niIiUUCnMQ0JCEBERgSlTpkAIIVveo0cPhIaGqq04IiIiejKVwvyll17CsmXL8PHHHyMjIwMlJSWw\nsrKCqampuusjIiKiJ3himD98+BAXLlyAoaEhXF1d0bp1a03URURERCpSGuY3btzA6NGjcfv2bQCA\ntbU1tm7diqZNm2qkOCIiInoypWezf/bZZ3BxccGZM2dw+vRptGrVCqtWrdJUbURERKQCpT3z5ORk\n7N27F40bNwYAhIWFYdiwYRopjIiIiFSjtGeem5sLc3Nz2eNmzZrh3r17ai+KiIiIVKc0zCUSiabq\nICIioqek0h3giIiIqO5SOmdeWlpaZY5c0bLIyEiVdrZkyRIkJydDIpEgLCwMjo6OVdZZuXIlfv/9\nd+zcuVOlbRIREb3olIZ5QEBAlaF2Kyurp9pRYmIi0tLSsGfPHqSmpiIsLAx79uyRW+f69etISkqC\noaHhU+2DiIjoRaQ0zJctW1ZrO4qPj4efnx8AwMbGBvn5+SgoKICxsbHc/qZNm4b169fX2n6JiIie\ndxqbM8/JyZG7/auZmRmys7Nlj2NiYtC+fXtYWFhoqiQiIqLngtZOgPvvF7bk5eUhJiYGo0aN0lY5\nREREOktjYW5ubo6cnBzZ47t378puRpOQkIDc3FwMGzYMkyZNQkpKCpYsWaKp0oiIiHSaxsK8Y8eO\niI2NBQCkpKTA3NxcNl/eo0cPHD16FHv37sX69ethb2+PsLAwTZVGRESk01T6CtRK6enpyMjIgIeH\nR4135OrqCnt7ewQFBUEikSA8PBwxMTEwMTFB165da7w9IiIiekSlMM/JycGMGTOQkJAAAwMDXLp0\nCXfv3sW7776LzZs3q3zS2owZM+QeK/o6VUtLS15jTkREVAMqDbMvWbIEhoaGOHjwIPT0Hj2lYcOG\ncHZ2rtXL14iIiKjmVOqZnz17FkePHkWjRo1kN5ExMjLCrFmz0LNnT7UWSERERMqp1DOXSqVy14hX\nMjAwQGFhYa0XRURERKpTKcxtbW2xf//+Ksu//PJL2NnZ1XpRREREpDqVhtknT56MCRMmICYmBmVl\nZZg4cSKuXr2KnJwcbNq0Sd01EhERkRIqhXmHDh2wb98+7N27F8bGxtDT04O/vz+CgoJ4+1UiIiIt\nU/k681atWvFGLkRERHWQSmE+e/Zspe1Lly6tlWKIiIio5lQK87S0NLnHUqkUGRkZKC8vR4cOHdRS\nGBEREalGpTCPioqqskwIgXXr1im8ZI2IiIg056m/aEUikWDChAnYsmVLbdZDRERENfRM35qWk5OD\nf//9t7ZqISIioqeg0jD7zJkzqywrLi7Gr7/+Cmdn51ovioiIiFSnUphnZWVVWfbSSy+hd+/eGDNm\nTK0XRURERKpTKcx37Ngh+4IVIiIiqltUmjN3dXVVdx1ERET0lFQO8++//17dtRAREdFTUGmYvUWL\nFggPD8fnn38OKysrGBoayrV/8sknaimOiIiInkylML927RpatmwJ4NHlaERERFR3qBTmO3fuVHcd\nRERE9JSUzpn37NlTU3UQERHRU1Ia5pmZmZqqg4iIiJ6S0jDnteVERER1n9I584qKCiQkJEAIoXQj\nHh4etVoUERERqU5pmJeXl2PUqFFKw1wikeDKlSu1XhgRERGpRmmYGxoa4vjx45qqhYiIiJ6C0jDX\n09ODhYWFpmohIiKip6D0BLgnzZUTERGR9ikN84CAAE3VQURERE9JaZgvXLhQU3UQERHRU1LpW9OI\niIio7mKYExER6TiGORERkY5jmBMREek4hjkREZGOY5gTERHpOIY5ERGRjmOYExER6TiGORERkY5j\nmBMREek4hjkREZGOY5gTERHpOIY5ERGRjmOYExER6TiGORERkY5jmBMREek4A03ubMmSJUhOToZE\nIkFYWBgcHR1lbQkJCVi1ahX09PRgbW2NxYsXQ0+PnzWIiIieRGNpmZiYiLS0NOzZsweLFy/G4sWL\n5drnzZuHtWvXIjo6Gg8fPsSZM2c0VRoREZFO01iYx8fHw8/PDwBgY2OD/Px8FBQUyNpjYmLQtGlT\nAICZmRnu37+vqdKIiIh0msbCPCcnB6amprLHZmZmyM7Olj02NjYGANy9exdnz56Fl5eXpkojIiLS\naVqblBZCVFl27949TJgwAeHh4XLBT0RERNXTWJibm5sjJydH9vju3bto3Lix7HFBQQHGjRuHqVOn\nwtPTU1NlERER6TyNhXnHjh0RGxsLAEhJSYG5ublsaB0Ali1bhpEjR6Jz586aKomIiOi5oLFL01xd\nXWFvb4+goCBIJBKEh4cjJiYGJiYm8PT0xIEDB5CWloZ9+/YBAHr37o3AwEBNlUdERKSzNHqd+YwZ\nM+Qet27dWvb7pUuXNFkKERHRc4N3ZSEiItJxDHMiIiIdxzAnIiLScQxzIiIiHccwJyIi0nEMcyIi\nIh3HMCciItJxDHMiIiIdxzAnIiLScQxzIiIiHccwJyIi0nEMcyIiIh3HMCciItJxDHMiIiIdxzAn\nIiLScQxzIiIiHccwJyIi0nEMcyIiIh3HMCciItJxDHMiIiIdxzAnIiLScQxzIiIiHccwJyIi0nEM\ncyIiIh3HMCciItJxDHMiIiIdxzAnIiLScQxzIiIiHccwJyIi0nEMcyIiIh3HMCciItJxDHMiIiId\nxzAnIiLScQxzIiKiJ9i4cSO6du2q7TKqxTAnIqIXRlFREdq1awcnJyfk5eWp/LwPPvgAcXFxaqzs\n2TDMiYjohfHdd9/B3NwcNjY2iImJ0XY5tYZhTkREL4zIyEj06dMHffv2RXR0NIQQsrYjR46gT58+\ncHFxQfv27TFp0iTcuXMHALBu3Tp07txZtm5ycjKGDx+O9u3bo127dhg3bhzS09Nl7T4+Pti2bRvm\nzZuH9u3bw93dHQsWLJDbX20yUMtWiYjouTZ/SwLOX7mjlX27vdUE4WM71Ph558+fx/Xr19G/f38Y\nGhpixYoVOHPmDDp37ow7d+7go48+wueff47OnTsjLy8Pc+fOxSeffIKVK1fKbae0tBTjx4/H4MGD\nsXXrVhQVFWHKlCmYPXs2du3aJVtvy5YtiIiIwLx585CQkIAxY8agU6dO8Pb2fuZj8DiGORERvRAi\nIyPRqVMnNGnSBADg6+uLqKgodO7cGQUFBaioqMDLL78MiUQCU1NTrFu3DhKJpMp2jIyMEBcXh5de\negkGBgYwMTGBr68vli1bJrde27Zt4efnBwDw9PSEmZkZ/vzzT4Y5ERHVDU/TM9amu3fvIi4uDmvW\nrJEtGzJkCMaOHYvMzEzY2NhgxIgRePfdd2Fra4sOHTqgZ8+ecHJyUri906dPY+vWrbh16xbKy8sh\nlUpRXl4ut84bb7wh9/jll19GUVFR7b84cM6ciIheAHv27EFZWRlmzZoFNzc3uLm5YcqUKZBKpYiO\njgYAzJkzB6dOncLw4cORlZWFYcOGYfXq1VW29csvv2DmzJno27cvfv75Z/zxxx+YO3dulfUU9erV\nhT1zIiJ6rpWVlWHPnj0YNWoUgoOD5dq++eYb7N27F5MnT0ZhYSGaNGmCgQMHYuDAgfjmm2+wdOlS\nTJs2Te45ycnJqF+/PkaNGiW3TJvYMycioudaXFwccnNzMXLkSFhaWsr9jBgxAg8ePMCRI0fQu3dv\nXLx4EUIIPHz4EJcuXULLli2rbK958+YoKipCSkoKHj58iN27d+PmzZsAgH/++UfTLw8Aw5yIiJ5z\nkZGR6NKlC5o1a1alrVGjRujatSuio6MxbNgwTJ06FU5OTvD19UVOTg5WrVpV5TndunVD//79MWLE\nCPj5+SE9PR0bN25Eq1at0Lt3b6SlpWniZcmRCHVd9KZGGRkZ8PX1xcmTJ2FpafnM2+sz/SAA4NDK\ngGfeFhERkTooyz72zImIiHScRk+AW7JkCZKTkyGRSBAWFgZHR0dZ27lz57Bq1Sro6+ujc+fOmDhx\nosbqcnuricb2RUREVNs0FuaJiYlIS0vDnj17kJqairCwMOzZs0fWvmjRInz11Vdo0qQJgoOD0b17\nd7Rq1Uojtena9ZJERET/pbFh9vj4eNmdcGxsbJCfn4+CggIAQHp6Oho0aIBmzZpBT08PXl5eiI+P\n11RpREREOk1jYZ6TkwNTU1PZYzMzM2RnZwMAsrOzYWZmprCNiIiIlNPaCXA6eBI9ERFRnaSxMDc3\nN0dOTo7s8d27d9G4cWOFbXfu3IG5ubmmSiMiItJpGgvzjh07IjY2FgCQkpICc3NzGBsbAwAsLS1R\nUFCAjIwMlJeX49SpU+jYsaOmSiMiItJpGjub3dXVFfb29ggKCoJEIkF4eDhiYmJgYmKCrl27IiIi\nAtOnTwcA+Pv7w9raWlOlERER6TSNXmc+Y8YMucetW7eW/d6uXTu5S9WIiIhINbwDHBERkY5jmBMR\nEek4hjkREZGOY5gTERHpOI2eAFdbKioqAAC3b9/WciVERESaUZl5lRn4XzoZ5pW3eh02bJiWKyEi\nItKs7OxsvPHGG3LLJEIH76taXFyMS5cuoXHjxtDX19d2OURERGpXUVGB7OxsODg44KWXXpJr08kw\nJyIiov/HE+CIiIh0HMOciIhIxzHMiYiIdBzDnIiISMe9kGG+ZMkSBAYGIigoCBcvXpRrO3fuHAYN\nGoTAwEBs2LBBSxXWfcqOYUJCAoYMGYKgoCDMnj0bUqlUS1XWbcqOYaWVK1di+PDhGq5Mdyg7hllZ\nWRg6dCgGDRqEefPmaalC3aDsOEZGRiIwMBBDhw7F4sWLtVRh3Xft2jX4+flh165dVdo0kiviBfPL\nL7+I8ePHCyGEuH79uhgyZIhce8+ePcU///wjKioqxNChQ8Vff/2ljTLrtCcdw65du4qsrCwhhBCT\nJ08Wp0+f1niNdd2TjqEQQvz1118iMDBQBAcHa7o8nfCkY/jhhx+KEydOCCGEiIiIEJmZmRqvURco\nO44PHjwQ3t7eoqysTAghxKhRo8Rvv/2mlTrrsocPH4rg4GDx8ccfi507d1Zp10SuvHA98/j4ePj5\n+QEAbGxskJ+fj4KCAgBAeno6GjRogGbNmkFPTw9eXl6Ij4/XZrl1krJjCAAxMTFo2rQpAMDMzAz3\n79/XSp112ZOOIQAsW7YM06ZN00Z5OkHZMZRKpfj111/h4+MDAAgPD8frr7+utVrrMmXH0dDQEIaG\nhigsLER5eTmKiorQoEEDbZZbJxkZGWHz5s0wNzev0qapXHnhwjwnJwempqayx2ZmZrI7ymVnZ8PM\nzExhG/0/ZccQAIyNjQEAd+/exdmzZ+Hl5aXxGuu6Jx3DmJgYtG/fHhYWFtooTycoO4a5ubmoX78+\nli5diqFDh2LlypXaKrPOU3Yc69Wrh4kTJ8LPzw/e3t5wcnKCtbW1tkqtswwMDKrcxKWSpnLlhQvz\nxwneM+eZKTqG9+7dw4QJExAeHi73RkGK/fcY5uXlISYmBqNGjdJiRbrnv8dQCIE7d+5gxIgR2LVr\nFy5fvozTp09rrzgd8t/jWFBQgC+++ALHjx/HyZMnkZycjKtXr2qxOqrOCxfm5ubmyMnJkT2+e/cu\nGjdurLDtzp07CodNXnTKjiHw6A1g3LhxmDp1Kjw9PbVRYp2n7BgmJCQgNzcXw4YNw6RJk5CSkoIl\nS5Zoq9Q6S9kxNDU1xeuvvw4rKyvo6+vDw8MDf/31l7ZKrdOUHcfU1FQ0b94cZmZmMDIygpubGy5d\nuqStUnWSpnLlhQvzjh07IjY2FgCQkpICc3Nz2bCwpaUlCgoKkJGRgfLycpw6dQodO3bUZrl1krJj\nCDya6x05ciQ6d+6srRLrPGXHsEePHjh69Cj27t2L9evXw97eHmFhYdost05SdgwNDAzQvHlz3Lp1\nS9bO4WHFlB1HCwsLpKamori4GABw6dIltGjRQlul6iRN5coLeW/2FStW4Pz585BIJAgPD8fly5dh\nYmKCrl27IikpCStWrAAAdOvWDWPGjNFytXVTdcfQ09MT7dq1g4uLi2zd3r17IzAwUIvV1k3K/g4r\nZWRkYPbs2di5c6cWK627lB3DtLQ0hIaGQggBW1tbREREQE/vheu/qETZcYyOjkZMTAz09fXh4uKC\nmTNnarvcOufSpUtYvnw5MjMzYWBggCZNmsDHxweWlpYay5UXMsyJiIieJ/yYSkREpOMY5kRERDqO\nYU5ERKTjGOZEREQ6jmFORESk4xjmRFoSGhqKoUOHaruMZ3LgwAG0adMGFRUVCttHjx6N2bNna7gq\nohcPL00jegrDhw/H+fPnYWBgUKUtODgYs2bNeuI2QkNDkZaWht27d6ujRNjZ2cHAwEB2bbWenh5e\nf/119OrVC+PGjUO9evVqfZ/nz59HWVkZPDw8an3bj8vIyICvry8MDQ0hkUhkyxs0aABXV1dMnToV\nLVu2VHl7Bw8ehKurK5o3b66OconUquo7ERGppFevXrIbQdRVERERGDx4MACgvLwcycnJmDp1KvLy\n8vDxxx/X+v62b9+Oli1baiTMK3355Zd45513ZI9v376N5cuXY9SoUTh8+DBMTEyeuA0hBJYuXYpV\nq1YxzEkncZidSE2ys7Mxbdo0dOzYES4uLhgwYADOnTuncF0hBNasWSP7ZqpOnTph6dKlKCsrAwBU\nVFRg/fr16N69O5ycnODr64stW7bUqB4DAwO0bdsWwcHBOHr0qGz5n3/+idGjR8Pd3R0uLi4YNWqU\n3JdpnDt3DoMHD0bbtm3h5uaGUaNG4fr16wAefbubnZ0dysvLERQUhBMnTmDz5s1wc3MD8GgEY8aM\nGbh58ybs7OyQmJgoV9O6devQpUsXSKVSFBcXY9GiRfDx8YGjoyN69uyJAwcO1Og1AkDTpk0RFhaG\n27dvy33V5FdffYXu3bvDxcUFXl5eWL16NYQQKCwsRJs2bXD//n2MHz8eEyZMAADcv38fs2bNgpeX\nF5ycnNC/f3/8+OOPNa6HSBMY5kRqMnfuXOTm5iI2NhaJiYno1KkTJk2aVOV7ywHg6NGj2LdvH7Zv\n347k5GTs2LEDp0+fxv79+wEA69evx4EDB7B27VpcuHABy5cvx+eff/5UYVdRUSGbHsjPz8fw4cPR\nqlUrnDx5EmfOnEHjxo0xevRoFBQUoKysDBMnTsTAgQORmJiI06dPw9raWmGvPjo6GhYWFhg3bhzO\nnz8v12ZtbQ1HR0ccO3ZMbvmRI0cQEBAAPT09zJs3D8nJydi+fTsuXLiAkJAQzJkzB0lJSTV+jaWl\npQAgm0qIjY3F6tWrsXLlSvz222/YsGEDtm3bhpiYGLzyyis4fvw4gEe9/E2bNgEAJk2ahPz8fOzf\nvx9JSUkYNGgQPvjgA6Snp9e4HiJ1Y5gTqcmaNWuwceNGGBsbw9DQEH369MHDhw9lvdr/+vfffyGR\nSACLkOoAAAYySURBVGThY21tjePHjyMoKAhSqRRRUVEYN24c7OzsoK+vDzc3NwwePBh79+5VuZ7S\n0lIkJSUhMjISAwcOBAAcOnQIEokEM2bMgLGxMYyNjREaGorc3Fz89NNPKC0tRUlJCerVqwd9fX0Y\nGxtj7ty5iI6OrvHx6Nu3L06cOAGpVAoAuHz5Mm7evIl+/fohLy8Phw4dwpQpU9C8eXMYGBiga9eu\n8PHxqdFrFEIgPT0dCxYsgKWlpWy438/PD2fOnIGDgwMAwMHBAW+++SaSk5MVbufq1as4f/48Zs2a\nhddeew1GRkYYNmwY7OzsZB+wiOoSzpkTPaUjR47Ivm3qv+bPn48BAwbg2rVrWLNmDVJSUvDw4UNZ\ne0lJSZXn9O7dG8ePH4evry9cXV3xzjvvoE+fPrCwsEBubi7y8vKwcOFCLFq0SPYcIYTcV88qEhER\ngQULFgB4NMxuaWmJ0aNHY+TIkQCAtLQ0WFlZwcjISPYcMzMzmJmZIT09HfXr10dISAjmzZuHL774\nAh4eHujatavcHLWqevXqhWXLliExMREdOnTA4cOH4ezsDGtrayQnJ0MqlWLChAlyJ7MJIeDk5KR0\nu+PHj5c9RyqVQgiBvn37YteuXbLXVVpainXr1uHkyZPIzc0FAJSVlaFVq1YKt3njxg0Ajz6A/JcQ\notrnEGkTw5zoKSk7Ae7BgwcYM2YMOnfujMOHD6Nx48a4ceMGevbsqXB9ExMTbN++HX/99Rd+/vln\nnDx5EuvXr8e6devQrl07AMDq1avlvlFNFf89AU6RkpISKLqgRSqVygJy7P+1d2+h0K1xHMe/NW85\nFgllHEooEQmRchxFTmkyUlwIY+SQS+coRSQ3asSFK1xwp5QLKWWcc4iauFAOIRFC5LDYF29mb5t3\n79d+07un/p/LtZ71zH/m5jfP86y1Hr0enU7H7OwsMzMzVFZWotFo6O7u/lQtLi4uxMbGMjExQXR0\nNBMTExgMBuDP6fDR0VGCgoI+1e9fb4Db2tpCp9ORmJiIh4eHpU1raysmkwmj0UhwcDAqleofd/J7\nrcdkMuHk5PSpeoT4HWSaXYgvsLOzw9XVFcXFxZbR88bGxg/bPzw8cHNzQ0BAAEVFRQwNDZGWlsbI\nyAiOjo64urpiNpvfXHNycmJZG/6vfH192dvbezNbcHp6ysXFhWX/7/Pzc5ydnS0j697eXsbHx7m8\nvPz052VnZzM1NcXq6ipnZ2ekp6cD4O3tjUqlevcdj46OeHp6+un+AwMDqaiooKWlhZOTE8vxtbU1\nUlNTCQ0NRaVS/XC549Xrnt1/r+fg4ODDPz9C/G4S5kJ8AbVajUqlYnV1lcfHR+bm5ixT8sfHx+/a\nt7a2Ul5eztHREfA9qHd3dy3PSRcWFjI8PMz8/DyKorC1tUV+fj4DAwO/VGdmZibPz890dXVxd3fH\n5eUl7e3tqNVq4uPjWVlZITk5GZPJhKIoPDw8sL6+jqur64cjVjs7O/b397m+vv7wRTIajYa7uzt6\nenrQaDSWPhwcHNDpdBiNRsxmM4qisLy8jFarfXPn/c8wGAx4enrS0NBgCV4fHx/MZjO3t7ccHh7S\n1NSEWq3m+PiYl5cX7O3tge/T69fX1/j5+REbG0tnZyd7e3soisLk5CQZGRmsrKx89mcW4stJmAvx\nBdzd3WlsbKS/v5+oqCiGhoZoa2sjLS2N5uZmxsbG3rSvra3Fy8uLnJwcQkNDycvLIyQkhOrqagBK\nSkooKCigvr6esLAwKisr0Wq1lJWV/VKdbm5uDAwMsL29TUJCAhkZGSiKwvDwMDY2NkRERFBXV0db\nWxvh4eHExcWxtLREX1/fm7XtV/n5+UxPT5OcnMzFxcW787a2tqSkpLCwsIBWq31zrr6+nqSkJPR6\nPeHh4TQ3N1NdXf1u3frffPv2jY6ODhYXFxkcHASgpqaG+/t7YmJiMBgMaLVaqqqq2NzcpLS0FBcX\nF7Kysujo6ECv1wPQ1dWFv78/ubm5REZGYjQa6ezstDx2J8T/ibwBTgghhLByMjIXQgghrJyEuRBC\nCGHlJMyFEEIIKydhLoQQQlg5CXMhhBDCykmYCyGEEFZOwlwIIYSwchLmQgghhJWTMBdCCCGs3B95\npU0xe7GeQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f124e89df90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAFtCAYAAAATY4N4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nImJzvwBAEowtZV7olwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12518ff2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAFtCAYAAAATY4N4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncRLmQgghRIqTMBdCCCFS3De7NAe5+TeMRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f125106b550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot ROC curves\n",
    "cols = ['Male', 'Asian', 'Black', 'White']\n",
    "for attr in cols:\n",
    "    target = Y_test[attr]\n",
    "    score = df_score[attr]\n",
    "    fig = plot_roc(attr, target, score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
